随机数据的最简约树
The most parsimonious tree for random data
摘要：
应用从随机数据重建系统发育树的方法提供了一种检测该方法是否对某些树“形状”具有固有偏差的方法。对于最大简约，应用于随机2状态数据序列，每个可能的二元系统发育树具有完全相同的简约分数分布。尽管这种令人愉快的和稍微令人惊讶的对称性，一些二元系统发育树比其他人更有可能是一个最简约（MP）树的序列$k$这样的字符，因为我们显示。对于$k=2$和六个分类群上的无根二叉树，任何具有毛虫形状的树比任何具有对称形状的树有更高的机会成为MP树。另一方面，如果我们采取任何两个二叉树，在任何数量的类群，我们证明，这两棵树之间的偏见消失的字符数量的增长。然而，又有一个扭曲：MP树上的6个分类群更有可能有一定的形状比一个均匀分布的二元系统发育树预测，这种差异似乎并没有消失$k$增长。
英文摘要：
Applying a method to reconstruct a phylogenetic tree from random data provides a way to detect whether that method has an inherent bias towards certain tree `shapes'. For maximum parsimony, applied to a sequence of random 2-state data, each possible binary phylogenetic tree has exactly the same distribution for its parsimony score. Despite this pleasing and slightly surprising symmetry, some binary phylogenetic trees are more likely than others to be a most parsimonious (MP) tree for a sequence of $k$ such characters, as we show. For $k=2$, and unrooted binary trees on six taxa, any tree with a caterpillar shape has a higher chance of being an MP tree than any tree with a symmetric shape. On the other hand, if we take any two binary trees, on any number of taxa, we prove that this bias between the two trees vanishes as the number of characters grows. However, again there is a twist: MP trees on six taxa are more likely to have certain shapes than a uniform distribution on binary phylogenetic trees predicts, and this difference does not appear to dissipate as $k$ grows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
